{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all of these libraries are used for plotting\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Define the grid on which we will evaluate our classifier\n",
    "# it ranges from -2 to 3 in x, -5 to 3 in y\n",
    "x_min, x_max = -1, 2.8\n",
    "y_min, y_max = -4, 2.8\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, .1),\n",
    "                     np.arange(y_min, y_max, .1))\n",
    "\n",
    "to_forward = np.array(list(zip(xx.ravel(), yy.ravel())))\n",
    "\n",
    "# Plot the dataset\n",
    "def plot_data(ax, X, Y):\n",
    "    plt.axis('off')\n",
    "    ax.scatter(X[:, 0], X[:, 1], s=1, c=Y, cmap='bone')\n",
    "    plt.axis([x_min, x_max, y_min, y_max])\n",
    "\n",
    "# plot the decision boundary of our classifier\n",
    "def plot_decision_boundary(ax, X, Y, classifier):\n",
    "    # forward pass on the grid, then convert to numpy for plotting\n",
    "    Z = classifier.forward(to_forward)\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    \n",
    "    # plot contour lines of the values of our classifier on the grid\n",
    "    ax.contourf(xx, yy, Z>0.5, cmap='Blues')\n",
    "    \n",
    "    # then plot the dataset\n",
    "    plot_data(ax, X,Y)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD8CAYAAABjAo9vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1044a1b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# load the data and visualize it\n",
    "data = np.load('./double_moon.npz')\n",
    "X = data['X']\n",
    "Y = data['Y']\n",
    "fig, ax = plt.subplots(1, 1, facecolor='#4B6EA9')\n",
    "ax.set_xlim(x_min, x_max)\n",
    "ax.set_ylim(y_min, y_max)\n",
    "plot_data(ax, X, Y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Sequential(object):\n",
    "    def __init__(self, layers):\n",
    "        self.layers = layers\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # compute the forward by applying the forward pass sequentially\n",
    "        return x\n",
    "    \n",
    "    def compute_loss(self, out, label):\n",
    "        # use the BCE loss\n",
    "        # -(label * log(output) + (1-label) * log(1-output))\n",
    "        # save the gradient, and return the loss\n",
    "        \n",
    "        # beware of dividing by zero in the gradient.\n",
    "        # split the computation in two cases, one where the label is 0 and another one where the label is 1\n",
    "        # add a small value (1e-10) to the denominator\n",
    "\n",
    "    def backward(self):\n",
    "        # apply backprop sequentially, starting from the gradient of the loss\n",
    "    \n",
    "    def step(self, learning_rate):\n",
    "        # take a gradient step for each layers\n",
    "\n",
    "class MyLinear(object):\n",
    "    def __init__(self, n_input, n_output):\n",
    "        # initialize two random matrices for A and b\n",
    "\n",
    "    def forward(self, x):\n",
    "        # save a copy of x, you'll need it for the backward\n",
    "        # return Ax + b\n",
    "\n",
    "    def backward(self, grad_output):\n",
    "        # y_i = \\sum_j A_{i,j} x_j + b_i\n",
    "\n",
    "        # d y_i / d A_{i, j} = x_j\n",
    "        # d loss / d y_i = grad_output[i]\n",
    "        # so d loss / d A_{i,j} = x_j * grad_output[i]  (by the chain rule)\n",
    "\n",
    "        # d y_i / d b_i = 1\n",
    "        # d loss / d y_i = grad_output[i]\n",
    "        \n",
    "        # now we need to compute the gradient with respect to x to continue the back propagation\n",
    "        # d y_i / d x_j = A_{i, j}\n",
    "        # to compute the gradient of the loss, we have to sum over all possible y_i in the chain rule\n",
    "        # d loss / d x_j = \\sum_i (d loss / d y_i) (d y_i / d x_j)\n",
    "    \n",
    "    def step(self, learning_rate):\n",
    "        # update self.A and self.b in the opposite direction of the stored gradients, for learning_rate\n",
    "    \n",
    "    \n",
    "class MyReLU(object):\n",
    "    def forward(self, x):\n",
    "        # the relu is y_i = max(0, x_i)\n",
    "    \n",
    "    def backward(self, grad_output):\n",
    "        # the gradient is 1 for the inputs that were above 0, 0 elsewhere\n",
    "    \n",
    "    def step(self, learning_rate):\n",
    "        # no need to do anything here, since ReLU has no parameters\n",
    "\n",
    "class MySigmoid(object):\n",
    "    def forward(self, x):\n",
    "        # the sigmoid is y_i = 1./(1+exp(-x_i))\n",
    "    \n",
    "    def backward(self, grad_output):\n",
    "        # the partial derivative is e^-x / (e^-x + 1)^2    (you can use wolframalpha for this)\n",
    "    \n",
    "    def step(self, learning_rate):\n",
    "        # no need to do anything here since Sigmoid has no parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "h=50\n",
    "\n",
    "# define your network with nn.sequential\n",
    "# it could be a linear layer with 2 inputs and h outputs, followed by a ReLU\n",
    "# then a linear layer with h inputs and 1 outputs, followed by a sigmoid\n",
    "# feel free to try other architectures\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, facecolor='#4B6EA9')\n",
    "ax.set_xlim(x_min, x_max)\n",
    "ax.set_ylim(y_min, y_max)\n",
    "losses = []\n",
    "for it in xrange(10000):\n",
    "    # pick a random example id \n",
    "\n",
    "    # select the corresponding example and label\n",
    "\n",
    "    # do a forward pass on the example\n",
    "\n",
    "    # compute the loss according to your output and the label\n",
    "\n",
    "    # backward pass\n",
    "\n",
    "    # gradient step\n",
    "\n",
    "    # draw the current decision boundary every 250 examples seen\n",
    "    if it % 250 == 0 : \n",
    "        plot_decision_boundary(ax, X,Y, net)\n",
    "        fig.canvas.draw()\n",
    "plot_decision_boundary(ax, X,Y, net)\n",
    "fig.canvas.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
